The AI of the future won’t just generate content—it will also need to understand physical spaces.
Maps, robots, self-driving cars, AR devices: all these applications rely on the same kind of thing—real-world data that’s continuously updated, accurate enough, and tied to specific locations.
The problem is that this kind of data isn’t as easy to obtain as text. Data for many places is updated very slowly, and public information is often incomplete. A model may know “what a shopping mall is,” but not necessarily which entrance at a particular mall is under construction today, which walkway is closed, or whether the surrounding area has changed.
That’s what interests me about the direction Vangrid is taking.
By collecting information about specific locations with phones and recording where the data comes from, anyone in the real world can become part of a data network. Instead of a handful of organizations capturing every place all at once, real-world environments can be continuously supplemented and updated.
That way, data is no longer just cold, one-off information—it comes closer to reflecting reality itself. For spatial models, robots, and AI that needs to understand places, this kind of data could be more valuable than an outdated map.
Of course, collection quality, privacy protection, and data verification are all important. A truly useful network isn’t about having as much data as possible; it’s about letting people know where the data came from, when it was collected, and whether it can actually be used.
AI can’t enter our daily lives with bigger models alone. It also needs a way to keep learning about the real world.
What Vangrid is working on is turning specific locations into data that AI can understand, verify, and use. Which use case do you think will take off first?
@vangrid_io
Maps, robots, self-driving cars, AR devices: all these applications rely on the same kind of thing—real-world data that’s continuously updated, accurate enough, and tied to specific locations.
The problem is that this kind of data isn’t as easy to obtain as text. Data for many places is updated very slowly, and public information is often incomplete. A model may know “what a shopping mall is,” but not necessarily which entrance at a particular mall is under construction today, which walkway is closed, or whether the surrounding area has changed.
That’s what interests me about the direction Vangrid is taking.
By collecting information about specific locations with phones and recording where the data comes from, anyone in the real world can become part of a data network. Instead of a handful of organizations capturing every place all at once, real-world environments can be continuously supplemented and updated.
That way, data is no longer just cold, one-off information—it comes closer to reflecting reality itself. For spatial models, robots, and AI that needs to understand places, this kind of data could be more valuable than an outdated map.
Of course, collection quality, privacy protection, and data verification are all important. A truly useful network isn’t about having as much data as possible; it’s about letting people know where the data came from, when it was collected, and whether it can actually be used.
AI can’t enter our daily lives with bigger models alone. It also needs a way to keep learning about the real world.
What Vangrid is working on is turning specific locations into data that AI can understand, verify, and use. Which use case do you think will take off first?
@vangrid_io